Engineering cost data storage processing method and device

Abstract

The invention relates to the technical field of computer information processing, in particular to a project cost data storage processing method and device.The project cost data storage processing device comprises a data acquisition layer, a data processing and storage layer and an application service layer, the data processing and storage layer comprises a preprocessing module, a UDM unified data module, a multi-dimensional cost database module and a real-time cost verification and deviation analysis module, and the application service layer comprises a cost early warning service module and a user interaction and API module. According to the method, standardized mapping of heterogeneous data is realized through a UDM (Unified Data Model), and manual intervention links are reduced by combining automatic engineering quantity extraction of the BIM analysis module and automatic price hanging of the price matching sub-module, so that the problems of quantity-price mismatching and data distortion in a traditional mode are thoroughly solved, and accurate data support is provided for cost accounting and decision making.

Description

Technical Field

[0001] This invention relates to the field of computer information processing technology, and in particular to a method and apparatus for storing and processing engineering cost data. Background Technology

[0002] Construction cost data involves core aspects such as project budgeting, cost accounting, and bidding, requiring the integration of multi-source data including design documents, bills of quantities, and material price databases. In the construction engineering field, cost management is a core aspect of project control, directly impacting the project's investment returns and implementation efficiency.

[0003] Traditional construction project cost management relies primarily on manual data entry and post-event accounting for data updates. This data lags behind project progress and fails to reflect real-time dynamic changes in project costs. By the time cost deviations are detected, irreversible cost overruns have often occurred, making it difficult to achieve the dynamic management goals of prevention and control during the project. Therefore, a method and device for storing and processing construction cost data is needed. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a method and apparatus for storing and processing engineering cost data.

[0005] The technical solution adopted in this invention is as follows: an engineering cost data storage and processing device, comprising a data acquisition layer, a data processing and storage layer, and an application service layer. The data acquisition layer includes a data acquisition module. The data processing and storage layer includes a preprocessing module, a UDM unified data module, a multi-dimensional cost database module, and a real-time cost verification and deviation analysis module. The application service layer includes a cost early warning service module and a user interaction and API module. The data acquisition module is used to receive BIM model files and market price data, and to perform preliminary verification of the integrity and format validity of the data. After removing abnormal data, the valid raw data is transmitted to the preprocessing module. The preprocessing module is used to parse, clean, and price match the raw data, providing basic data for subsequent standardized mapping; The UDM unified data module is used to map heterogeneous data into a unified format, establish data association relationships, and output standardized data. The multi-dimensional cost database module is used to store standardized data, pricing data and verification data in a multi-dimensional relational structure, and simultaneously perform blockchain notarization to support efficient data query, traceability and audit verification. The real-time cost verification and deviation analysis module is used to calculate the deviation rate between the current cost data and the comparison benchmark, and compare it with the preset threshold to determine whether to trigger an early warning. The cost early warning service module is used to receive early warning trigger signals, generate early warning information, and push it to users; The user interaction and API module is used to receive user operation instructions and provide feedback on the execution results, generate multi-dimensional analysis reports, and support export.

[0006] As a further description of the above technical solution: The UDM unified data module has a built-in standardized data dictionary and association rule base. The data dictionary is used to define the unified field names, data types and unit standards of core cost elements such as engineering quantity, material price, labor unit price, machinery shift fee and indirect cost to ensure data format consistency. The association rule base is used to define the mapping relationship between components and resources, engineering quantity and price and consumption quotas to provide rule support for data association and matching.

[0007] As a further description of the above technical solution: The preprocessing module includes a built-in BIM parsing submodule and a price matching submodule. The BIM parsing submodule is used to parse BIM model files in IFC and RVT formats, automatically extract the geometric attributes, non-geometric attributes, and quantity data of components, and assign a temporary identifier code to each component to achieve automated quantity extraction. The price matching submodule is used to automatically match standardized resource prices with corresponding quantities according to the association rules provided by the UDM unified data module, and calculate the comprehensive unit price and total price of each component or list item according to a preset formula to achieve automated quantity-price association.

[0008] As a further description of the above technical solution: The multidimensional cost database module adopts a combined architecture of graph database and time-series database. The graph database is used to store multi-dimensional relationships between components, resources, time, contracts, and regions, constructing a data association network and supporting complex dimension queries. The time-series database is used to store time-series related cumulative values ​​of engineering quantities, cumulative values ​​of costs, and price fluctuation data to meet the needs of time-dimensional analysis. The multidimensional cost database module has a blockchain notarization function, which is used to enforce encryption and notarization of key data such as component IDs, core engineering quantities, comprehensive unit prices, and total prices, ensuring that the data is tamper-proof and improving audit reliability and traceability.

[0009] As a further description of the above technical solution: The real-time cost verification and deviation analysis module is used to preset three sets of cost thresholds, namely, the deviation threshold from the budget baseline, the deviation threshold from historical data of similar projects, and the deviation threshold from the market average data. The cost thresholds can be customized by the user. The real-time cost verification and deviation analysis module is also used to retrieve the current cost data and corresponding comparison benchmark data from the multi-dimensional cost database module in real time, calculate the deviation rate, compare the deviation rate with the preset thresholds, and generate early warning trigger signals or verification records based on the comparison results.

[0010] A method for storing and processing engineering cost data, based on the aforementioned engineering cost data storage and processing device, includes the following steps: S101: Data Acquisition and Preliminary Verification. The data acquisition module receives BIM model files and market price data, verifies the integrity and format validity of the data, provides feedback on abnormal data to the user and requests correction, and transmits valid raw data to the preprocessing module. S102: BIM model parsing and quantity extraction. The BIM parsing submodule of the preprocessing module parses the BIM model file, extracts the geometric attributes, non-geometric attributes and quantity data of the components, assigns temporary identification codes to the components, and transmits the relevant data to the preprocessing core unit. S103: Data cleaning and standardization mapping. The preprocessing core unit cleans the data. The UDM unified data module calls the data dictionary and association rule base to map heterogeneous data into a unified format, assign unique component IDs and resource IDs, establish data association relationships, and transmit standardized data to the multidimensional cost database module and price matching submodule. S104: Price matching and comprehensive unit price generation. The price matching submodule automatically matches standardized resource prices with corresponding engineering quantities according to association rules, calculates comprehensive unit price and total price according to preset formulas, and transmits the listed price data to the multi-dimensional cost database module. S105: Multi-dimensional structured storage and evidence preservation. The multi-dimensional cost database module stores standardized data and pricing data in a multi-dimensional associated structure according to components, resources, time, contracts, and regions. Simultaneously, it performs blockchain evidence preservation on key data such as component ID, core engineering quantity data, comprehensive unit price, and total price, generates evidence preservation hash values, and associates and stores them with the corresponding data in the database. S106: Real-time cost verification and deviation analysis. The real-time cost verification and deviation analysis module retrieves the current cost data and comparison benchmark data from the multi-dimensional cost database module, calculates the deviation rate, and compares it with the preset threshold to determine whether to trigger an early warning. S107: Cost early warning push and recording. If the deviation rate exceeds the threshold, the cost early warning service module receives the early warning trigger signal, determines the early warning level, and generates early warning information containing deviation details, impact analysis, and handling suggestions. The information is pushed to the user through user interaction and the API module, and the early warning record is stored in the multi-dimensional cost database module. S108: Multi-dimensional analysis report generation and export. Users send report generation commands through user interaction and API modules. This module extracts corresponding data from the multi-dimensional cost database module and generates multi-dimensional visual analysis reports, which can be viewed online or exported to third-party systems.

[0011] As a further description of the above technical solution: The data cleaning in step S103 includes using a 3σ outlier detection algorithm to remove extreme price data, deleting duplicate data using a hash table, and unifying non-standard units into preset standard units. The standardization mapping is implemented through the standardized data dictionary and association rule base of the UDM unified data module to ensure that data from different sources and formats have a unified structure and clear association relationships, providing a foundation for subsequent storage and analysis.

[0012] As a further description of the above technical solution: The comprehensive unit price calculation formula in step S104 is: Comprehensive unit price = (material cost + labor cost + machinery cost) × (1 + indirect cost rate) × (1 + profit margin) × (1 + tax rate); where material cost, labor cost, and machinery cost are calculated by the price matching submodule based on the component's engineering quantity, corresponding resource price, and consumption quota provided by the UDM unified data module. The price matching submodule is used to automatically complete the quantity-price correlation calculation and reduce manual intervention.

[0013] As a further description of the above technical solution: The blockchain notarization process in step S105 is as follows: After receiving standardized data and pricing data, the multi-dimensional cost database module automatically performs hash calculations on key information such as component ID, core engineering quantity data, comprehensive unit price, and total price to generate a unique notarization hash value; the notarization hash value is uploaded to the blockchain network through the consortium link interface to complete the notarization; at the same time, the notarization hash value is associated with the corresponding complete cost data in the database and stored to ensure the immutability of key cost data and the traceability of the entire process, meeting the audit verification requirements.

[0014] The present invention has the following beneficial effects: 1. This invention achieves standardized mapping of heterogeneous data through the Unified Data Model (UDM). Combined with the automated quantity extraction of the BIM parsing module and the automatic pricing of the price matching submodule, it reduces manual intervention and completely solves the problems of quantity-price mismatch and data distortion in traditional models. This provides accurate data support for cost accounting and decision-making. Furthermore, the UDM unified data model constructs a standardized cost data knowledge base, enabling seamless integration and cross-platform sharing of data from different projects and stages. Users can quickly access historical project data through API interfaces for benchmarking analysis and experience reuse, reducing repetitive work and trial-and-error costs. Simultaneously, the blockchain notarization function ensures the immutability of key data, improving the reliability of data auditing and serving as valid evidence in project audits and dispute resolution, thus reducing legal risks.

[0015] 2. The real-time cost verification and deviation analysis module of this invention achieves millisecond-level data verification, and the cost early warning service module pushes early warning information in a short time, transforming the traditional post-event accounting into a dynamic management mode of pre-event prevention and in-event control. By setting preset thresholds and comparing multiple benchmarks, the risk of cost overruns can be identified in advance, effectively ensuring the achievement of project investment control objectives.

[0016] 3. The multi-dimensional cost database module of this invention constructs a multi-dimensional relational architecture of components, resources, time, contracts, and regions, breaking the storage limitations of traditional single-dimensional databases. Users can flexibly select one or more dimensions for cross-analysis, such as analyzing cost proportions by component type, tracking cost accumulation by time schedule, and optimizing configuration schemes by resource type, providing project managers with a comprehensive and three-dimensional cost perspective, significantly improving the scientific and rational nature of decision-making. At the same time, the multi-dimensional cost database module adopts a distributed storage architecture, combined with data encryption processing, effectively avoiding the risk of data loss and corruption associated with traditional local server storage. It supports mainstream BIM model formats such as IFC and RVT, and is compatible with the API interfaces of third-party project management systems and financial systems, achieving seamless data integration and collaborative work without requiring significant modifications to the existing management system, thus reducing the threshold and cost of system implementation. Attached Figure Description

[0017] Figure 1 This is a modular schematic diagram of an engineering cost data storage and processing device proposed in this invention; Figure 2 This is a flowchart of a method for storing and processing engineering cost data according to the present invention. Detailed Implementation

[0018] Reference Figure 1The present invention provides an engineering cost data storage and processing device, comprising a data acquisition layer, a data processing and storage layer, and an application service layer. The data acquisition layer includes a data acquisition module, the data processing and storage layer includes a preprocessing module, a UDM unified data module, a multidimensional cost database module, and a real-time cost verification and deviation analysis module, and the application service layer includes a cost early warning service module and a user interaction and API module.

[0019] Data Acquisition Module: Deployed in the data acquisition layer, it is used to receive BIM model files (supporting formats such as IFC and RVT) and market price data (including manually entered data and data accessed through third-party interfaces), perform preliminary integrity checks on the received data, remove data with format errors or missing key fields, and transmit valid raw data to the preprocessing module; Preprocessing module: Deployed in the data processing and storage layer, with built-in BIM parsing submodule and price matching submodule; the BIM parsing submodule is used to parse BIM model files, automatically extract the geometric attributes, non-geometric attributes and quantity data of components, and assign temporary identification codes to components; the price matching submodule is used to receive standardized data, realize the automatic matching of price and quantity according to the pricing rules, and generate comprehensive unit price and total price. UDM Unified Data Module: Deployed in the data processing and storage layer, it has a built-in standardized data dictionary and association rule base. The data dictionary defines the unified fields, data types, and unit standards of core cost elements (work volume, material price, labor unit price, etc.). The association rule base defines the mapping relationship between components and resources, and between work volume and price. It is used to map pre-processed heterogeneous data into a unified format, assign unique component IDs and resource IDs, and establish data association relationships. Multidimensional cost database module: Deployed in the data processing and storage layer, it adopts a combined architecture of graph database and time-series database. It is used to store standardized data, listing data and verification data according to the multi-dimensional association structure of component-resource-time-contract-region, and simultaneously executes blockchain notarization to ensure that key data cannot be tampered with. Real-time cost verification and deviation analysis module: Deployed in the data processing and storage layer, it is used to preset cost thresholds (based on historical data, industry benchmarks or user-defined), retrieve data from the multi-dimensional cost database module in real time, calculate the deviation rate between the current cost data and the comparison benchmark, and determine whether to trigger an early warning after comparing it with the threshold. Cost early warning service module: Deployed at the application service layer, it is used to receive early warning trigger signals, determine the early warning level based on the deviation rate, generate early warning information including deviation details, impact analysis and handling suggestions, and push it to users through the user interaction module; User Interaction and API Module: Deployed at the application service layer, it serves as the interaction interface between users and the system. It receives user operation commands (query, generate reports, adjust thresholds, etc.), converts the commands into system signals, and transmits them to the corresponding modules. At the same time, it extracts database data, generates multi-dimensional visual analysis reports, and supports exporting reports to third-party systems.

[0020] Specifically, The data acquisition layer and processing layer work together: After receiving the BIM model file and market price data, the data acquisition module transmits the data to the preprocessing module via TCP / IP protocol. A checksum mechanism is used during transmission to ensure data integrity. If data is missing or formatted incorrectly, the data acquisition module provides feedback to the user through user interaction and the API module. The data is then corrected and retransmitted to ensure the validity of the original data received by the preprocessing module.

[0021] The preprocessing module's internal structure and its coordination with other modules: After receiving the raw data, the preprocessing module first activates the BIM parsing submodule to parse the BIM model, extract component information and quantity data, generate intermediate data containing temporary codes, and transmit it to the preprocessing core unit. After the core unit completes data cleaning, it pushes the data to the UDM unified data module. After the UDM engine completes standardized mapping, it transmits the associated data to the multidimensional cost database module for storage and synchronous execution of data preservation. At the same time, it transmits standardized price data and component-resource association relationships to the price matching submodule to provide data support for automatic pricing.

[0022] The integration of the UDM engine and the multidimensional database: The UDM unified data module, as the core of data standardization, provides a unified data storage standard for the multidimensional cost database module through its built-in data dictionary and association rule base. The engine transmits the standardized data to the multidimensional database according to the structure of component ID-resource ID-relationship. The database automatically establishes nodes (components, resources, time, etc.) and edges (relationships) based on the preset multidimensional storage architecture, achieving structured data storage. Simultaneously, it triggers a blockchain notarization process to encrypt and notarize key data, ensuring the synchronization of data storage and notarization.

[0023] The real-time verification module works in conjunction with the database and early warning modules: The real-time cost verification and deviation analysis module monitors data updates from the multi-dimensional cost database module in real time via a long connection. When new pricing data or updated data is stored and verified, the module immediately retrieves relevant benchmark data (budget baseline, historical data, market data) to calculate deviations. If the deviation rate exceeds a threshold, the module pushes a deviation analysis report and early warning trigger signal to the cost early warning service module via a message queue, while simultaneously writing the verification results back to the multi-dimensional database, forming a data closed loop.

[0024] The application service layer and the processing and storage layer work together as follows: After receiving the warning trigger signal, the cost warning service module generates warning information according to preset rules and pushes it to the user interaction and API module via the HTTP interface. The module then pushes the warning information to the user in the form of pop-ups, SMS messages, or APP notifications. User commands such as queries and report generation sent through the interaction module are converted into database query statements or module control signals and transmitted to the multidimensional cost database module or corresponding functional modules. After execution, the results are fed back to the user, realizing two-way interaction. If data authenticity needs to be verified during the query process, it can be verified in the blockchain network through the evidence storage hash value associated with the database.

[0025] Reference Figure 2 The present invention provides a method for storing and processing engineering cost data, comprising the following steps: S101: Data Acquisition and Preliminary Verification The data acquisition module supports three data input methods: local file upload, FTP remote transfer, and third-party API access. For BIM model files, the module first verifies whether the file format is supported (IFC, RVT, etc.) and parses the file header information to determine if there is any corruption. For market price data, it verifies whether it contains necessary fields such as name, specifications, unit price, and unit, and uses regular expressions to validate the data format (e.g., whether the unit price is a numeric type). Raw data that passes the verification is marked as valid and transmitted to the preprocessing module; data that fails the verification is marked as abnormal, and the reasons for the abnormality are displayed through the user interface (e.g., "Missing material specification field" or "BIM file corrupted"). The user must correct the data and resubmit it for verification.

[0026] S102: BIM Model Analysis and Quantity Extraction After the BIM parsing submodule starts, it first calls an open-source BIM parsing library (such as IfcOpenShell) to parse the BIM model file, identifying the component types (walls, beams, columns, floor slabs, etc.) and extracting the geometric parameters (length, width, height, volume, etc.) and non-geometric attributes (material, construction process, floor, etc.) of each component. Based on the component's geometric parameters and the corresponding quantity calculation rules (e.g., concrete components are calculated by volume, walls by area), the quantity data is automatically calculated. Simultaneously, a unique temporary identifier code is assigned to each component using the UUID algorithm for subsequent data association. After parsing, the BIM parsing submodule transmits the associated data of the temporary code, component information, and quantity to the preprocessing core unit.

[0027] S103: Data Cleaning and Standardization Mapping The preprocessing core unit first cleans the market price data: extreme values ​​are removed using the 3σ principle, duplicate data entries are deleted using a hash table, and units of different formats are standardized. Subsequently, the core unit transmits the cleaned component-quantity data and resource-price data to the UDM unified data module. The engine calls the standardized data dictionary to map fields such as component type, quantity indicators, and resource type to a unified standard, assigning unique permanent component IDs and resource IDs; based on the association rule base, it establishes the mapping relationship between component IDs and resource IDs, generating a standardized dataset, which is then transmitted to the multidimensional cost database module and the price matching submodule, respectively.

[0028] S104: Price Matching and Composite Unit Price Generation After receiving the standardized dataset, the price matching submodule queries the resource ID associated with each component ID and its corresponding price data according to the pricing rules in the association rule base. It then calculates the itemized cost based on the component's engineering quantity data. Subsequently, the price matching submodule calculates the comprehensive unit price according to the preset formula (comprehensive unit price = (material cost + labor cost + machinery cost) × (1 + indirect cost rate) × (1 + profit margin) × (1 + tax rate)). After the pricing is completed, the price matching submodule transmits the data of component ID - resource ID - itemized cost - comprehensive unit price - total price to the multidimensional cost database module.

[0029] S105: Multidimensional Structured Storage and Evidence Preservation The multidimensional cost database module uses Neo4j graph database to store multidimensional relationships and InfluxDB time-series database to store time-series data. In the graph database, component IDs, resource IDs, contract IDs, region IDs, and time nodes serve as nodes, with node attributes including corresponding codes, names, and parameters. Relationships such as consumption (components and resources), ownership (components and regions), and execution (components and time) serve as edges, with edge attributes including association rules and consumption quotas. The time-series database stores the cumulative project quantity, cumulative cost, and price fluctuation data for each time node, tagged with timestamps. Simultaneously, the multi-dimensional cost database module executes the blockchain notarization process: it automatically extracts key data such as component ID, core engineering quantity data, comprehensive unit price, and total price, and calculates a unique notarization hash value using the SHA-256 hash algorithm; it uploads the notarization hash value to the blockchain network through a preset consortium link interface, and the blockchain network records the notarization time, notarization subject, and hash value to form an immutable notarization record; the database establishes an association index between the notarization hash value and the corresponding complete cost data and stores it, ensuring that the authenticity of the data can be quickly verified in the blockchain network through the hash value in the future, meeting the data traceability needs in scenarios such as project auditing and dispute resolution.

[0030] S106: Real-time Cost Verification and Deviation Analysis The real-time cost verification and deviation analysis module has three preset thresholds: a deviation threshold from the budget baseline, a deviation threshold from historical data of similar projects, and a deviation threshold from the market average. These thresholds can be customized by the user through an interactive module. The module retrieves the current component unit price, budget baseline unit price, historical average unit price of similar projects, and market average unit price from the multi-dimensional cost database module in real time, and calculates the deviation rate for each. The deviation rate includes the deviation rate from the budget baseline, the deviation rate from historical data, and the deviation rate from market data. The module compares the deviation rate with the thresholds. If the deviation rate exceeds the threshold, the module generates a deviation analysis report and sends an early warning trigger signal to the cost early warning service module.

[0031] S107: Cost Warning Push and Recording After receiving the warning trigger signal, the cost early warning service module generates early warning information based on the deviation rate. The early warning information is pushed to the user's computer client and mobile APP through user interaction and API module. At the same time, information such as early warning time, early warning level, deviation analysis report, and processing status (unprocessed) is stored in the early warning record table of the multi-dimensional cost database module to support subsequent traceability. The early warning record is synchronously associated with the storage hash value of the corresponding data to ensure the authenticity of the early warning basis.

[0032] S108: Generation and Export of Multidimensional Analysis Reports Users select a combination of component type and timeline analysis dimensions through the interactive module and send a report generation command. The interactive module converts the command into a database query, retrieving data such as the quantity of work, cost percentage, and deviations for each component type at different construction stages from the multi-dimensional cost database module. The system then uses the ECharts visualization tool to convert the data into bar charts (cost percentage of each component), line charts (cumulative cost trends at each stage), and tables (details of component deviations), generating a multi-dimensional analysis report. Key data in the report are labeled with corresponding notarized hash values, allowing users to verify data authenticity on the blockchain network. Users can view the report online through the interactive interface or export it to the project management system in Excel and PDF formats via API, providing data support for project schedule adjustments and resource optimization.

[0033] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data storage and processing device for engineering cost estimates, characterized in that, It includes a data acquisition layer, a data processing and storage layer, and an application service layer. The data acquisition layer includes a data acquisition module. The data processing and storage layer includes a preprocessing module, a UDM unified data module, a multi-dimensional cost database module, and a real-time cost verification and deviation analysis module. The application service layer includes a cost early warning service module and a user interaction and API module. The data acquisition module is used to receive BIM model files and market price data, and to perform preliminary verification of the integrity and format validity of the data. After removing abnormal data, the valid raw data is transmitted to the preprocessing module. The preprocessing module is used to parse, clean, and price match the raw data, providing basic data for subsequent standardized mapping; The UDM unified data module is used to map heterogeneous data into a unified format, establish data association relationships, and output standardized data. The multi-dimensional cost database module is used to store standardized data, pricing data and verification data in a multi-dimensional relational structure, and simultaneously perform blockchain notarization to support efficient data query, traceability and audit verification. The real-time cost verification and deviation analysis module is used to calculate the deviation rate between the current cost data and the comparison benchmark, and compare it with the preset threshold to determine whether to trigger an early warning. The cost early warning service module is used to receive early warning trigger signals, generate early warning information, and push it to users; The user interaction and API module is used to receive user operation instructions and provide feedback on the execution results, generate multi-dimensional analysis reports, and support export.

2. The engineering cost data storage and processing device according to claim 1, characterized in that: The UDM unified data module has a built-in standardized data dictionary and association rule library; the data dictionary is used to define the unified field names, data types and unit standards of core cost elements such as project quantity, material price, labor unit price, machinery shift fee, and indirect cost to ensure data format consistency; The association rule base is used to define the mapping relationship between components and resources, engineering quantity and price, and consumption quota, providing rule support for data association and matching.

3. The engineering cost data storage and processing device according to claim 1, characterized in that: The preprocessing module includes a built-in BIM parsing submodule and a price matching submodule. The BIM parsing submodule is used to parse BIM model files in IFC and RVT formats, automatically extract the geometric attributes, non-geometric attributes, and quantity data of components, and assign a temporary identifier code to each component to achieve automated quantity extraction. The price matching submodule is used to automatically match standardized resource prices with corresponding quantities according to the association rules provided by the UDM unified data module, and calculate the comprehensive unit price and total price of each component or list item according to a preset formula to achieve automated quantity-price association.

4. The engineering cost data storage and processing device according to claim 1, characterized in that: The multidimensional cost database module adopts a combined architecture of graph database and time-series database. The graph database is used to store multi-dimensional relationships between components, resources, time, contracts, and regions, constructing a data association network and supporting complex dimension queries. The time-series database is used to store time-series related cumulative values ​​of engineering quantities, cumulative values ​​of costs, and price fluctuation data to meet the needs of time-dimensional analysis. The multidimensional cost database module has a blockchain notarization function, which is used to enforce encryption and notarization of key data such as component IDs, core engineering quantities, comprehensive unit prices, and total prices, ensuring that the data is tamper-proof and improving audit reliability and traceability.

5. The engineering cost data storage and processing device according to claim 1, characterized in that: The real-time cost verification and deviation analysis module is used to preset three sets of cost thresholds, namely, the deviation threshold from the budget baseline, the deviation threshold from historical data of similar projects, and the deviation threshold from the market average data. The cost thresholds can be customized by the user. The real-time cost verification and deviation analysis module is also used to retrieve the current cost data and corresponding comparison benchmark data from the multi-dimensional cost database module in real time, calculate the deviation rate, compare the deviation rate with the preset thresholds, and generate early warning trigger signals or verification records based on the comparison results.

6. A method for storing and processing engineering cost data, characterized in that, Based on the engineering cost data storage and processing device according to any one of claims 1-5, the process includes the following steps: S101: Data Acquisition and Preliminary Verification. The data acquisition module receives BIM model files and market price data, verifies the integrity and format validity of the data, provides feedback on abnormal data to the user and requests correction, and transmits valid raw data to the preprocessing module. S102: BIM model parsing and quantity extraction. The BIM parsing submodule of the preprocessing module parses the BIM model file, extracts the geometric attributes, non-geometric attributes and quantity data of the components, assigns temporary identification codes to the components, and transmits the relevant data to the preprocessing core unit. S103: Data cleaning and standardization mapping. The preprocessing core unit cleans the data. The UDM unified data module calls the data dictionary and association rule base to map heterogeneous data into a unified format, assign unique component IDs and resource IDs, establish data association relationships, and transmit standardized data to the multidimensional cost database module and price matching submodule. S104: Price matching and comprehensive unit price generation. The price matching submodule automatically matches standardized resource prices with corresponding engineering quantities according to association rules, calculates comprehensive unit price and total price according to preset formulas, and transmits the listed price data to the multi-dimensional cost database module. S105: Multi-dimensional structured storage and evidence preservation. The multi-dimensional cost database module stores standardized data and pricing data in a multi-dimensional associated structure according to components, resources, time, contracts, and regions. Simultaneously, it performs blockchain evidence preservation on key data such as component ID, core engineering quantity data, comprehensive unit price, and total price, generates evidence preservation hash values, and associates and stores them with the corresponding data in the database. S106: Real-time cost verification and deviation analysis. The real-time cost verification and deviation analysis module retrieves the current cost data and comparison benchmark data from the multi-dimensional cost database module, calculates the deviation rate, and compares it with the preset threshold to determine whether to trigger an early warning. S107: Cost early warning push and recording. If the deviation rate exceeds the threshold, the cost early warning service module receives the early warning trigger signal, determines the early warning level, and generates early warning information containing deviation details, impact analysis, and handling suggestions. The information is pushed to the user through user interaction and the API module, and the early warning record is stored in the multi-dimensional cost database module. S108: Multi-dimensional analysis report generation and export. Users send report generation commands through user interaction and API modules. This module extracts corresponding data from the multi-dimensional cost database module and generates multi-dimensional visual analysis reports, which can be viewed online or exported to third-party systems.

7. The method for storing and processing engineering cost data according to claim 6, characterized in that: The data cleaning in step S103 includes using a 3σ outlier detection algorithm to remove extreme price data, deleting duplicate data using a hash table, and unifying non-standard units into preset standard units. The standardization mapping is implemented through the standardized data dictionary and association rule base of the UDM unified data module to ensure that data from different sources and formats have a unified structure and clear association relationships, providing a foundation for subsequent storage and analysis.

8. The method for storing and processing engineering cost data according to claim 6, characterized in that: The comprehensive unit price calculation formula in step S104 is: Comprehensive unit price = (material cost + labor cost + machinery cost) × (1 + indirect cost rate) × (1 + profit margin) × (1 + tax rate); where material cost, labor cost, and machinery cost are calculated by the price matching submodule based on the component's engineering quantity, corresponding resource price, and consumption quota provided by the UDM unified data module. The price matching submodule is used to automatically complete the quantity-price correlation calculation and reduce manual intervention.

9. A method for storing and processing engineering cost data according to claim 6, characterized in that: The blockchain notarization process in step S105 is as follows: After receiving standardized data and pricing data, the multi-dimensional cost database module automatically performs hash calculations on key information such as component ID, core engineering quantity data, comprehensive unit price, and total price to generate a unique notarization hash value; the notarization hash value is uploaded to the blockchain network through the consortium link interface to complete the notarization; at the same time, the notarization hash value is associated with the corresponding complete cost data in the database and stored to ensure the immutability of key cost data and the traceability of the entire process, meeting the audit verification requirements.

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